Large digital libraries can contain more music, films, products or articles than anyone could browse manually. Recommendation systems reduce that complexity by predicting what might be relevant.

Those predictions are usually based on signals such as previous activity, similarity between items and broader patterns among users.

Recommendations are useful, but actively searching outside them can still reveal material that an automated system would not have chosen.